MPDB_kerats_only_analysis
#these libraries are ported here in case you remove the code and place elsewhere.
# install.packages('Signac','Seurat') # These packages often appear to be outdated/broken, so this line ensures up to date libraries before code running.
#Load Libraries
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(Seurat)Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Attaching splibrary(patchwork)
library(data.table)data.table 1.14.2 using 1 threads (see ?getDTthreads). Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********
Attaching package: ‘data.table’
The following objects are masked from ‘package:dplyr’:
between, first, lastlibrary(stringr)
library(ggplot2)Want to understand how all the pieces fit together? Read R for
Data Science: https://r4ds.had.co.nz/library(ggpubr)
library(openxlsx)# set working directory
#set working directory
setwd("~/OneDrive - Queen Mary, University of London/QMUL/Lab/Coding/data/R/Spatial/Scripts")
# load .rds file with pre-filtered data.
kerats <- readRDS(file = "keratinocytes_only.rds")
keratsAn object of class Seurat
16812 features across 1929 samples within 1 assay
Active assay: RNA (16812 features, 2000 variable features)
2 dimensional reductions calculated: pca, umap# Visualize QC metrics as a violin plot
VlnPlot(kerats, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
summary(kerats@meta.data) orig.ident nCount_RNA nFeature_RNA percent.mt
Length:1929 Min. : 700 Min. : 501 Min. :0.222
Class :character 1st Qu.: 1099 1st Qu.: 652 1st Qu.:1.824
Mode :character Median : 1644 Median : 898 Median :2.557
Mean : 3901 Mean :1284 Mean :2.597
3rd Qu.: 3211 3rd Qu.:1450 3rd Qu.:3.353
Max. :63451 Max. :7362 Max. :4.997
RNA_snn_res.1.5 seurat_clusters RNA_snn_res.0.8
0 :375 0 :593 0 :593
1 :220 1 :460 1 :460
2 :217 2 :450 2 :450
4 :186 5 :232 5 :232
5 :179 7 :194 7 :194
6 :168 3 : 0 3 : 0
(Other):584 (Other): 0 (Other): 0
#run the PCA analysis of the dataset
kerats <- RunPCA(kerats, features = VariableFeatures(object = kerats))Warning in PrepDR(object = object, features = features, verbose = verbose) :
The following 1 features requested have zero variance (running reduction without them): Gm35167
PC_ 1
Positive: Fxyd2, Krt9, Atp1b1, Gpi1, Ftl1, Krt5, Atp2a3, Pglyrp1, Wfdc2, Mylpf
Plin2, Myl9, Rps18, Krt8, Tagln, Acta2, Slc12a2, Atp1a1, Krt19, Serpinf1
Hbb-bs, Tnni2, Igfbp4, Lyz2, Krt79, Htra3, Apod, Mfsd4a, Cldn3, Col3a1
Negative: Rnf227, Lce1m, Lce1d, Sdcbp2, Cpa4, Lce6a, Csta1, Gm94, Lce1c, Lce1i
Lce1a1, Flg, Atg9b, Smpd3, Lce1f, Kprp, Lce1b, Lce1h, Lce1g, Lce1e
2310050C09Rik, Nt5c3, Hrnr, Asprv1, Yod1, Lce1j, Lce1l, Mapk13, Lce1k, Lce1a2
PC_ 2
Positive: Flg, Hrnr, Asprv1, Cdsn, Crct1, Lce1a2, Lce1b, 2310050C09Rik, Wfdc12, Lce1g
Lce1a1, Lce1h, Lce1l, Krt9, Lce1d, Lce1c, Kprp, Gm94, Mt4, Lce1i
Krtdap, Krt24, Lce1f, Lce1e, Dsg1a, Sprr1b, 2310046K23Rik, Lce1j, Krt2, Krt16
Negative: Atp1a1, Pi16, Ftl1, Eno1, Serping1, Gpi1, Myoc, Timp3, Apod, Sparc
Igfbp6, Serpinf1, Fxyd2, Tnxb, Acta2, Ccdc80, Atp2a3, Pmp22, Car12, Selenop
Fn1, Clec3b, Fmod, Myl9, Atp1b1, Pcolce, Igfbp5, Plin2, Cd248, Pfkp
PC_ 3
Positive: Flg2, Lce1m, Gba2, Tsc22d2, Txnrd1, Plcxd1, Sdcbp2, Lce6a, Kctd4, 2210017I01Rik
Col1a1, Aif1l, Gpsm1, Fmo2, Tbc1d9, Rnf227, Plcxd2, Gm38119, Col1a2, Ccdc80
Smpd3, S100a8, Klk9, 3300005D01Rik, Col3a1, 4930511M18Rik, Plk3, Cfd, Gramd1c, Gm4316
Negative: Krt9, Krt24, Mt4, 4930438A08Rik, Dapl1, Dsg1a, Sprr1b, Rps24, Sfn, Rps27a
Dbi, Iffo2, Krt6a, Serpinb5, Odc1, Rpl36a, Rps12, Rps20, Rpl23, Wfdc12
Rpl30, Rpl21, Rps15a, Aqp3, Rpl9, Rps18, Rpl22, Rpl12, Rpl10, Rps2
PC_ 4
Positive: Krt9, Krt16, Krt6b, Krt6a, Wfdc12, Gjb2, Tmem229b, S100a9, S100a8, Spink12
Rptn, Defb3, S100a7a, Igfbp2, Sprr1a, Sprr1b, Klk14, 2300002M23Rik, Wfdc18, Lce3c
Psors1c2, Pla2g4d, Serpina11, Akr1d1, 4930438A08Rik, Lce3f, Rnase2b, Sprr2k, Serpinb3a, 4833423E24Rik
Negative: Krt2, Ggct, Flg2, Orm1, Lce1m, Krtdap, Oas1f, Alox12e, Susd2, Casp14
Hal, Ano9, Tmprss4, Abhd12b, Wfdc21, Urah, Skint3, Krt15, Rdh9, Akr1c18
Ivl, Hcar2, Scgb1a1, Gan, Gm20627, Slc5a9, Smagp, Dapl1, Il20rb, Klk7
PC_ 5
Positive: Krt9, Mapk13, Tbx1, Crnn, Spink7, Btc, Tbc1d23, 4930511M18Rik, 2310050C09Rik, Dmpk
Serpinb7, Xist, Dapl1, Gadd45a, Xkrx, Tmem229b, Igfbp2, Odc1, Iffo2, Nlrp10
Txnrd1, Lce1k, Krt24, Rnf227, Map3k8, Plcxd1, 1810037I17Rik, Atg9b, Stk10, 4930438A08Rik
Negative: S100a8, S100a9, Sprr2d, Cstdc5, Krt16, Krt6b, Sprr2b, Rptn, Psors1c2, Klk14
Arg1, Lce3a, 2300002M23Rik, Col3a1, Lce3f, Lce3b, Col1a1, Krtdap, Sprr2h, Degs2
Slpi, Fetub, Stfa3, Rnase2b, 2610528A11Rik, Urah, Klk9, Uox, Sprr2i, Col1a2 # Examine and visualize PCA results a few different ways
print(kerats[["pca"]], dims = 1:5, nfeatures = 5)PC_ 1
Positive: Fxyd2, Krt9, Atp1b1, Gpi1, Ftl1
Negative: Rnf227, Lce1m, Lce1d, Sdcbp2, Cpa4
PC_ 2
Positive: Flg, Hrnr, Asprv1, Cdsn, Crct1
Negative: Atp1a1, Pi16, Ftl1, Eno1, Serping1
PC_ 3
Positive: Flg2, Lce1m, Gba2, Tsc22d2, Txnrd1
Negative: Krt9, Krt24, Mt4, 4930438A08Rik, Dapl1
PC_ 4
Positive: Krt9, Krt16, Krt6b, Krt6a, Wfdc12
Negative: Krt2, Ggct, Flg2, Orm1, Lce1m
PC_ 5
Positive: Krt9, Mapk13, Tbx1, Crnn, Spink7
Negative: S100a8, S100a9, Sprr2d, Cstdc5, Krt16
#elbow plot , shows SD of PCs, elbow is where significance should begin to be negligible.
ElbowPlot(kerats)
#pick 4 PCs#Here is where we optimise the number of PCs used to cluster the cells, and the resolution of the clustering algorithm.
kerats <- FindNeighbors(kerats, dims = 1:4)Computing nearest neighbor graph
Computing SNNkerats <- FindClusters(kerats, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1929
Number of edges: 54403
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8151
Number of communities: 7
Elapsed time: 0 secondshead(Idents(kerats), 5)2.1_AAACCGTTCGTCCAGG-1 2.1_AAACTGCTGGCTCCAA-1 2.1_AAATCGTGTACCACAA-1
6 1 2
2.1_AACACACGCTCGCCGC-1 2.1_AACCGCTAAGGGATGC-1
1 1
Levels: 0 1 2 3 4 5 6#Plot UMAP
kerats <- RunUMAP(kerats, dims = 1:5)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
10:27:46 UMAP embedding parameters a = 0.9922 b = 1.112
10:27:46 Read 1929 rows and found 5 numeric columns
10:27:46 Using Annoy for neighbor search, n_neighbors = 30
10:27:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:27:47 Writing NN index file to temp file /var/folders/17/t98thp9n3zb7xfm2pnqtq84w0000gn/T//RtmpIQk9Fw/file1f4f73a1a0e0
10:27:47 Searching Annoy index using 1 thread, search_k = 3000
10:27:47 Annoy recall = 100%
10:27:48 Commencing smooth kNN distance calibration using 1 thread
10:27:49 Initializing from normalized Laplacian + noise
10:27:49 Commencing optimization for 500 epochs, with 71552 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:27:52 Optimization finishedDimPlot(kerats, reduction = "umap")
DimPlot(kerats, reduction = "pca")
# find markers for every cluster compared to all remaining cells, report only the positive ones using only.pos = TRUE
kerats.markers <- FindAllMarkers(kerats, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
For a more efficient implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the limma package
--------------------------------------------
install.packages('BiocManager')
BiocManager::install('limma')
--------------------------------------------
After installation of limma, Seurat will automatically use the more
efficient implementation (no further action necessary).
This message will be shown once per session
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~03s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~01s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s kerats.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_log2FC)
cluster_markers <- kerats.markers %>% group_by(cluster) %>% filter(!between(avg_log2FC, -0.6, 0.6) & p_val_adj < 0.05)
top_markers <- kerats.markers %>% group_by(cluster) %>% filter(!between(avg_log2FC, -1, 1) & p_val_adj < 0.05)
#write out markers to excel file. Cosnider using CSV as these are easier to manipulate.
write.xlsx(cluster_markers,"kerats_allcluster_markers.xlsx")
# https://scrnaseq-course.cog.sanger.ac.uk/website/seurat-chapter.html
# has a tutorial explaining these things#Lower Keratinocyte markers
VlnPlot(kerats, features = c("Krt10"))
VlnPlot(kerats, features = c("Krt1"))
VlnPlot(kerats, features = c("Dsp"))
VlnPlot(kerats, features = c("Krt14"))
#upper epidermis
VlnPlot(kerats, features = c("Lor"))
VlnPlot(kerats, features = c("Flg"))
VlnPlot(kerats, features = c("Hrnr"))
VlnPlot(kerats, features = c("Cdsn"))
VlnPlot(kerats, features = c("Ivl"))
VlnPlot(kerats, features = c("Slurp1"))
VlnPlot(kerats, features = c("Crnn"))
DimPlot(kerats)
FeaturePlot(kerats, features = "Lor", label = TRUE)
FeaturePlot(kerats, features = "Flg", label = TRUE)
FeaturePlot(kerats, features = "Lce1m", label = TRUE)
FeaturePlot(kerats, features = "Trp63", label = TRUE)
#actin binding genes and GOIs
VlnPlot(kerats, features = c("Sptbn2"))
VlnPlot(kerats, features = c("Amotl2"))
VlnPlot(kerats, features = c("Anxa1"))
VlnPlot(kerats, features = c("Tmsb4x"))
VlnPlot(kerats, features = c("Lmna"))
NA
NA
NA
NA
NADimPlot(kerats)
FeaturePlot(kerats, features = "Sptbn2", label = TRUE)
FeaturePlot(kerats, features = "Amotl2", label = TRUE)
FeaturePlot(kerats, features = "Tmsb4x", label = TRUE)
FeaturePlot(kerats, features = "Anxa1", label = TRUE)
kerat_marker_gene <- "Lor"
list_of_features <- c("Sptbn2","Tmsb4x","Amotl2","Anxa1")
# Parse list_of_features to featurescatter
for (gene in list_of_features){
p <- FeatureScatter(kerats, feature1 = (kerat_marker_gene), feature2 = (gene))
print(p)
}
# Use this line to see single feature plot, for ease.
#FeatureScatter(kerats, feature1 = kerat_marker_gene, feature2 = "Sptbn2")# #Dermal Fib markers
# # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6204438/#:~:text=These%20different%20dermal%20fibroblast%20cell,et%20al.%2C%202018).
# VlnPlot(kerats, features = c("Dpp4")) #CD26
#
# VlnPlot(kerats, features = c("Thy1")) #CD90 - dermal fib marker
# VlnPlot(kerats, features = c("Vim"))
#
# #cluster 4 is fibroblasts, need to remove. # #SET IDENTS THAT ARE TO BE REMOVED BASED ON THIS INFO
# extracted_clusters <- c("0", "1", "2", "3", "5")
#
# #subset seurat object
# keratinocytes <- subset(kerats, idents = extracted_clusters)
# keratinocytes
#
# saveRDS(keratinocytes, file = "keratinocytes_only.rds")# set working directory
#set working directory
setwd("~/OneDrive - Queen Mary, University of London/QMUL/Lab/Coding/data/R/Spatial/Scripts")
# load .rds file with pre-filtered data.
kerats <- readRDS(file = "keratinocytes_only.rds")
keratsAn object of class Seurat
16812 features across 1929 samples within 1 assay
Active assay: RNA (16812 features, 2000 variable features)
2 dimensional reductions calculated: pca, umap# Visualize QC metrics as a violin plot
VlnPlot(kerats, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
summary(kerats@meta.data) orig.ident nCount_RNA nFeature_RNA percent.mt
Length:1929 Min. : 700 Min. : 501 Min. :0.222
Class :character 1st Qu.: 1099 1st Qu.: 652 1st Qu.:1.824
Mode :character Median : 1644 Median : 898 Median :2.557
Mean : 3901 Mean :1284 Mean :2.597
3rd Qu.: 3211 3rd Qu.:1450 3rd Qu.:3.353
Max. :63451 Max. :7362 Max. :4.997
RNA_snn_res.1.5 seurat_clusters RNA_snn_res.0.8
0 :375 0 :593 0 :593
1 :220 1 :460 1 :460
2 :217 2 :450 2 :450
4 :186 5 :232 5 :232
5 :179 7 :194 7 :194
6 :168 3 : 0 3 : 0
(Other):584 (Other): 0 (Other): 0
#run the PCA analysis of the dataset
kerats <- RunPCA(kerats, features = VariableFeatures(object = kerats))Warning in PrepDR(object = object, features = features, verbose = verbose) :
The following 1 features requested have zero variance (running reduction without them): Gm35167
PC_ 1
Positive: Fxyd2, Krt9, Atp1b1, Gpi1, Ftl1, Krt5, Atp2a3, Pglyrp1, Wfdc2, Mylpf
Plin2, Myl9, Rps18, Krt8, Tagln, Acta2, Slc12a2, Atp1a1, Krt19, Serpinf1
Hbb-bs, Tnni2, Igfbp4, Lyz2, Krt79, Htra3, Apod, Mfsd4a, Cldn3, Col3a1
Negative: Rnf227, Lce1m, Lce1d, Sdcbp2, Cpa4, Lce6a, Csta1, Gm94, Lce1c, Lce1i
Lce1a1, Flg, Atg9b, Smpd3, Lce1f, Kprp, Lce1b, Lce1h, Lce1g, Lce1e
2310050C09Rik, Nt5c3, Hrnr, Asprv1, Yod1, Lce1j, Lce1l, Mapk13, Lce1k, Lce1a2
PC_ 2
Positive: Flg, Hrnr, Asprv1, Cdsn, Crct1, Lce1a2, Lce1b, 2310050C09Rik, Wfdc12, Lce1g
Lce1a1, Lce1h, Lce1l, Krt9, Lce1d, Lce1c, Kprp, Gm94, Mt4, Lce1i
Krtdap, Krt24, Lce1f, Lce1e, Dsg1a, Sprr1b, 2310046K23Rik, Lce1j, Krt2, Krt16
Negative: Atp1a1, Pi16, Ftl1, Eno1, Serping1, Gpi1, Myoc, Timp3, Apod, Sparc
Igfbp6, Serpinf1, Fxyd2, Tnxb, Acta2, Ccdc80, Atp2a3, Pmp22, Car12, Selenop
Fn1, Clec3b, Fmod, Myl9, Atp1b1, Pcolce, Igfbp5, Plin2, Cd248, Pfkp
PC_ 3
Positive: Flg2, Lce1m, Gba2, Tsc22d2, Txnrd1, Plcxd1, Sdcbp2, Lce6a, Kctd4, 2210017I01Rik
Col1a1, Aif1l, Gpsm1, Fmo2, Tbc1d9, Rnf227, Plcxd2, Gm38119, Col1a2, Ccdc80
Smpd3, S100a8, Klk9, 3300005D01Rik, Col3a1, 4930511M18Rik, Plk3, Cfd, Gramd1c, Gm4316
Negative: Krt9, Krt24, Mt4, 4930438A08Rik, Dapl1, Dsg1a, Sprr1b, Rps24, Sfn, Rps27a
Dbi, Iffo2, Krt6a, Serpinb5, Odc1, Rpl36a, Rps12, Rps20, Rpl23, Wfdc12
Rpl30, Rpl21, Rps15a, Aqp3, Rpl9, Rps18, Rpl22, Rpl12, Rpl10, Rps2
PC_ 4
Positive: Krt9, Krt16, Krt6b, Krt6a, Wfdc12, Gjb2, Tmem229b, S100a9, S100a8, Spink12
Rptn, Defb3, S100a7a, Igfbp2, Sprr1a, Sprr1b, Klk14, 2300002M23Rik, Wfdc18, Lce3c
Psors1c2, Pla2g4d, Serpina11, Akr1d1, 4930438A08Rik, Lce3f, Rnase2b, Sprr2k, Serpinb3a, 4833423E24Rik
Negative: Krt2, Ggct, Flg2, Orm1, Lce1m, Krtdap, Oas1f, Alox12e, Susd2, Casp14
Hal, Ano9, Tmprss4, Abhd12b, Wfdc21, Urah, Skint3, Krt15, Rdh9, Akr1c18
Ivl, Hcar2, Scgb1a1, Gan, Gm20627, Slc5a9, Smagp, Dapl1, Il20rb, Klk7
PC_ 5
Positive: Krt9, Mapk13, Tbx1, Crnn, Spink7, Btc, Tbc1d23, 4930511M18Rik, 2310050C09Rik, Dmpk
Serpinb7, Xist, Dapl1, Gadd45a, Xkrx, Tmem229b, Igfbp2, Odc1, Iffo2, Nlrp10
Txnrd1, Lce1k, Krt24, Rnf227, Map3k8, Plcxd1, 1810037I17Rik, Atg9b, Stk10, 4930438A08Rik
Negative: S100a8, S100a9, Sprr2d, Cstdc5, Krt16, Krt6b, Sprr2b, Rptn, Psors1c2, Klk14
Arg1, Lce3a, 2300002M23Rik, Col3a1, Lce3f, Lce3b, Col1a1, Krtdap, Sprr2h, Degs2
Slpi, Fetub, Stfa3, Rnase2b, 2610528A11Rik, Urah, Klk9, Uox, Sprr2i, Col1a2 # Examine and visualize PCA results a few different ways
print(kerats[["pca"]], dims = 1:5, nfeatures = 5)PC_ 1
Positive: Fxyd2, Krt9, Atp1b1, Gpi1, Ftl1
Negative: Rnf227, Lce1m, Lce1d, Sdcbp2, Cpa4
PC_ 2
Positive: Flg, Hrnr, Asprv1, Cdsn, Crct1
Negative: Atp1a1, Pi16, Ftl1, Eno1, Serping1
PC_ 3
Positive: Flg2, Lce1m, Gba2, Tsc22d2, Txnrd1
Negative: Krt9, Krt24, Mt4, 4930438A08Rik, Dapl1
PC_ 4
Positive: Krt9, Krt16, Krt6b, Krt6a, Wfdc12
Negative: Krt2, Ggct, Flg2, Orm1, Lce1m
PC_ 5
Positive: Krt9, Mapk13, Tbx1, Crnn, Spink7
Negative: S100a8, S100a9, Sprr2d, Cstdc5, Krt16
#elbow plot , shows SD of PCs, elbow is where significance should begin to be negligible.
ElbowPlot(kerats)
#pick 6 PCs#Here is where we optimise the number of PCs used to cluster the cells, and the resolution of the clustering algorithm.
kerats <- FindNeighbors(kerats, dims = 1:6)Computing nearest neighbor graph
Computing SNNkerats <- FindClusters(kerats, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1929
Number of edges: 58306
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7987
Number of communities: 6
Elapsed time: 0 secondshead(Idents(kerats), 5)2.1_AAACCGTTCGTCCAGG-1 2.1_AAACTGCTGGCTCCAA-1 2.1_AAATCGTGTACCACAA-1
3 2 1
2.1_AACACACGCTCGCCGC-1 2.1_AACCGCTAAGGGATGC-1
2 2
Levels: 0 1 2 3 4 5#Plot UMAP
kerats <- RunUMAP(kerats, dims = 1:5)10:29:26 UMAP embedding parameters a = 0.9922 b = 1.112
10:29:26 Read 1929 rows and found 5 numeric columns
10:29:26 Using Annoy for neighbor search, n_neighbors = 30
10:29:26 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:29:27 Writing NN index file to temp file /var/folders/17/t98thp9n3zb7xfm2pnqtq84w0000gn/T//RtmpIQk9Fw/file1f4f3f77af30
10:29:27 Searching Annoy index using 1 thread, search_k = 3000
10:29:27 Annoy recall = 100%
10:29:28 Commencing smooth kNN distance calibration using 1 thread
10:29:29 Initializing from normalized Laplacian + noise
10:29:29 Commencing optimization for 500 epochs, with 71552 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:29:33 Optimization finishedDimPlot(kerats, reduction = "umap")
DimPlot(kerats, reduction = "pca")
#Keratinocyte markers
VlnPlot(kerats, features = c("Lor"))
VlnPlot(kerats, features = c("Ivl"))
VlnPlot(kerats, features = c("Flg"))
VlnPlot(kerats, features = c("Krt10"))
VlnPlot(kerats, features = c("Krt5"))
VlnPlot(kerats, features = c("Slurp1"))
VlnPlot(kerats, features = c("Crnn"))
NA
NA
NA#Keratinocyte markers
VlnPlot(kerats, features = c("Sptbn2"))
VlnPlot(kerats, features = c("Amotl2"))
VlnPlot(kerats, features = c("Anxa1"))
VlnPlot(kerats, features = c("Tmsb4x"))
VlnPlot(kerats, features = c("Lmna"))
NA
NA
NA
NA
NADimPlot(kerats)
FeaturePlot(kerats, features = "Lor", label = TRUE)
FeaturePlot(kerats, features = "Flg", label = TRUE)
FeaturePlot(kerats, features = "Lce1m", label = TRUE)
FeaturePlot(kerats, features = "Trp63", label = TRUE)
DimPlot(kerats)
FeaturePlot(kerats, features = "Anxa1", label = TRUE)
FeaturePlot(kerats, features = "Sptbn2", label = TRUE)
FeaturePlot(kerats, features = "Amotl2", label = TRUE)
FeaturePlot(kerats, features = "Tmsb4x", label = TRUE)
# find markers for every cluster compared to all remaining cells, report only the positive ones using only.pos = TRUE
kerats.markers <- FindAllMarkers(kerats, only.pos = FALSE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~05s
|+++ | 6 % ~04s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 11% ~04s
|+++++++ | 13% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|+++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|+++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 3 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~13s
|++ | 3 % ~12s
|+++ | 4 % ~12s
|+++ | 5 % ~11s
|++++ | 6 % ~11s
|++++ | 7 % ~11s
|+++++ | 8 % ~11s
|+++++ | 9 % ~11s
|++++++ | 10% ~10s
|++++++ | 11% ~10s
|+++++++ | 12% ~10s
|+++++++ | 13% ~10s
|++++++++ | 14% ~10s
|++++++++ | 15% ~10s
|+++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|++++++++++ | 18% ~09s
|++++++++++ | 19% ~09s
|+++++++++++ | 20% ~09s
|+++++++++++ | 21% ~09s
|++++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|++++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|+++++++++++++++ | 28% ~09s
|+++++++++++++++ | 29% ~09s
|++++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|+++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~07s
|++++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|++++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|+++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~06s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~04s
|+++++ | 9 % ~04s
|+++++ | 10% ~04s
|++++++ | 11% ~04s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s kerats.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_log2FC)
cluster_markers <- kerats.markers %>% group_by(cluster) %>% filter(!between(avg_log2FC, -0.6, 0.6) & p_val_adj < 0.05)
top_markers <- kerats.markers %>% group_by(cluster) %>% filter(!between(avg_log2FC, -1, 1) & p_val_adj < 0.05)
#write out markers to excel file. Cosnider using CSV as these are easier to manipulate.
write.xlsx(cluster_markers,"refined_kerats_allcluster_markers.xlsx")
# https://scrnaseq-course.cog.sanger.ac.uk/website/seurat-chapter.html
# has a tutorial explaining these 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oKIyBQYXJzZSBsaXN0X29mX2ZlYXR1cmVzIHRvIGZlYXR1cmVzY2F0dGVyCgpmb3IgKGdlbmUgaW4gbGlzdF9vZl9mZWF0dXJlcyl7CgogIHAgPC0gRmVhdHVyZVNjYXR0ZXIoa2VyYXRzLCBmZWF0dXJlMSA9IChrZXJhdF9tYXJrZXJfZ2VuZSksIGZlYXR1cmUyID0gKGdlbmUpKQogIHByaW50KHApCn0KCiMgVXNlIHRoaXMgbGluZSB0byBzZWUgc2luZ2xlIGZlYXR1cmUgcGxvdCwgZm9yIGVhc2UuIAojRmVhdHVyZVNjYXR0ZXIoa2VyYXRzLCBmZWF0dXJlMSA9IGtlcmF0X21hcmtlcl9nZW5lLCBmZWF0dXJlMiA9ICJTcHRibjIiKQpgYGAKCgoKYGBge3J9CiMgI0Rlcm1hbCBGaWIgbWFya2VycwojICMgaHR0cHM6Ly93d3cubmNiaS5ubG0ubmloLmdvdi9wbWMvYXJ0aWNsZXMvUE1DNjIwNDQzOC8jOn46dGV4dD1UaGVzZSUyMGRpZmZlcmVudCUyMGRlcm1hbCUyMGZpYnJvYmxhc3QlMjBjZWxsLGV0JTIwYWwuJTJDJTIwMjAxOCkuCiMgVmxuUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gYygiRHBwNCIpKSAjQ0QyNgojIAojIFZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIlRoeTEiKSkgI0NEOTAgLSBkZXJtYWwgZmliIG1hcmtlcgojIFZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIlZpbSIpKQojIAojICNjbHVzdGVyIDQgaXMgZmlicm9ibGFzdHMsIG5lZWQgdG8gcmVtb3ZlLiAKYGBgCgpgYGB7cn0KIyAjU0VUIElERU5UUyBUSEFUIEFSRSBUTyBCRSBSRU1PVkVEIEJBU0VEIE9OIFRISVMgSU5GTwojIGV4dHJhY3RlZF9jbHVzdGVycyA8LSBjKCIwIiwgIjEiLCAiMiIsICIzIiwgIjUiKQojIAojICNzdWJzZXQgc2V1cmF0IG9iamVjdAojIGtlcmF0aW5vY3l0ZXMgPC0gc3Vic2V0KGtlcmF0cywgaWRlbnRzID0gZXh0cmFjdGVkX2NsdXN0ZXJzKQojIGtlcmF0aW5vY3l0ZXMKIyAKIyBzYXZlUkRTKGtlcmF0aW5vY3l0ZXMsIGZpbGUgPSAia2VyYXRpbm9jeXRlc19vbmx5LnJkcyIpCmBgYAoKCmBgYHtyfQojIHNldCB3b3JraW5nIGRpcmVjdG9yeQojc2V0IHdvcmtpbmcgZGlyZWN0b3J5IApzZXR3ZCgifi9PbmVEcml2ZSAtIFF1ZWVuIE1hcnksIFVuaXZlcnNpdHkgb2YgTG9uZG9uL1FNVUwvTGFiL0NvZGluZy9kYXRhL1IvU3BhdGlhbC9TY3JpcHRzIikKCiMgbG9hZCAucmRzIGZpbGUgd2l0aCBwcmUtZmlsdGVyZWQgZGF0YS4gCmtlcmF0cyA8LSByZWFkUkRTKGZpbGUgPSAia2VyYXRpbm9jeXRlc19vbmx5LnJkcyIpCgprZXJhdHMKYGBgCgpgYGB7cn0KIyBWaXN1YWxpemUgUUMgbWV0cmljcyBhcyBhIHZpb2xpbiBwbG90ClZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIm5GZWF0dXJlX1JOQSIsICJuQ291bnRfUk5BIiwgInBlcmNlbnQubXQiKSwgbmNvbCA9IDMpCgpzdW1tYXJ5KGtlcmF0c0BtZXRhLmRhdGEpCmBgYAoKYGBge3J9CgojcnVuIHRoZSBQQ0EgYW5hbHlzaXMgb2YgdGhlIGRhdGFzZXQKa2VyYXRzIDwtIFJ1blBDQShrZXJhdHMsIGZlYXR1cmVzID0gVmFyaWFibGVGZWF0dXJlcyhvYmplY3QgPSBrZXJhdHMpKQoKIyBFeGFtaW5lIGFuZCB2aXN1YWxpemUgUENBIHJlc3VsdHMgYSBmZXcgZGlmZmVyZW50IHdheXMKcHJpbnQoa2VyYXRzW1sicGNhIl1dLCBkaW1zID0gMTo1LCBuZmVhdHVyZXMgPSA1KQpgYGAKCgpgYGB7cn0KCiNlbGJvdyBwbG90ICwgc2hvd3MgU0Qgb2YgUENzLCBlbGJvdyBpcyB3aGVyZSBzaWduaWZpY2FuY2Ugc2hvdWxkIGJlZ2luIHRvIGJlIG5lZ2xpZ2libGUuCkVsYm93UGxvdChrZXJhdHMpCgojcGljayA2IFBDcwpgYGAKYGBge3J9CiNIZXJlIGlzIHdoZXJlIHdlIG9wdGltaXNlIHRoZSBudW1iZXIgb2YgUENzIHVzZWQgdG8gY2x1c3RlciB0aGUgY2VsbHMsIGFuZCB0aGUgcmVzb2x1dGlvbiBvZiB0aGUgY2x1c3RlcmluZyBhbGdvcml0aG0uCmtlcmF0cyA8LSBGaW5kTmVpZ2hib3JzKGtlcmF0cywgZGltcyA9IDE6NikKa2VyYXRzIDwtIEZpbmRDbHVzdGVycyhrZXJhdHMsIHJlc29sdXRpb24gPSAwLjUpCgpoZWFkKElkZW50cyhrZXJhdHMpLCA1KQpgYGAKCmBgYHtyfQojUGxvdCBVTUFQCmtlcmF0cyA8LSBSdW5VTUFQKGtlcmF0cywgZGltcyA9IDE6NSkKRGltUGxvdChrZXJhdHMsIHJlZHVjdGlvbiA9ICJ1bWFwIikKRGltUGxvdChrZXJhdHMsIHJlZHVjdGlvbiA9ICJwY2EiKQpgYGAKCmBgYHtyfQojS2VyYXRpbm9jeXRlIG1hcmtlcnMKVmxuUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gYygiTG9yIikpClZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIkl2bCIpKQpWbG5QbG90KGtlcmF0cywgZmVhdHVyZXMgPSBjKCJGbGciKSkKClZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIktydDEwIikpClZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIktydDUiKSkKVmxuUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gYygiU2x1cnAxIikpClZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIkNybm4iKSkKCgoKYGBgCgpgYGB7cn0KI0tlcmF0aW5vY3l0ZSBtYXJrZXJzClZsblBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9IGMoIlNwdGJuMiIpKQpWbG5QbG90KGtlcmF0cywgZmVhdHVyZXMgPSBjKCJBbW90bDIiKSkKVmxuUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gYygiQW54YTEiKSkKVmxuUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gYygiVG1zYjR4IikpCgpWbG5QbG90KGtlcmF0cywgZmVhdHVyZXMgPSBjKCJMbW5hIikpCgoKCgoKYGBgCmBgYHtyfQpEaW1QbG90KGtlcmF0cykKRmVhdHVyZVBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9ICJMb3IiLCBsYWJlbCA9IFRSVUUpCkZlYXR1cmVQbG90KGtlcmF0cywgZmVhdHVyZXMgPSAiRmxnIiwgbGFiZWwgPSBUUlVFKQoKCkZlYXR1cmVQbG90KGtlcmF0cywgZmVhdHVyZXMgPSAiTGNlMW0iLCBsYWJlbCA9IFRSVUUpCgpGZWF0dXJlUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gIlRycDYzIiwgbGFiZWwgPSBUUlVFKQpgYGAKYGBge3J9CkRpbVBsb3Qoa2VyYXRzKQpGZWF0dXJlUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gIkFueGExIiwgbGFiZWwgPSBUUlVFKQpGZWF0dXJlUGxvdChrZXJhdHMsIGZlYXR1cmVzID0gIlNwdGJuMiIsIGxhYmVsID0gVFJVRSkKRmVhdHVyZVBsb3Qoa2VyYXRzLCBmZWF0dXJlcyA9ICJBbW90bDIiLCBsYWJlbCA9IFRSVUUpCkZlYXR1cmVQbG90KGtlcmF0cywgZmVhdHVyZXMgPSAiVG1zYjR4IiwgbGFiZWwgPSBUUlVFKQpgYGAKCmBgYHtyfQojIGZpbmQgbWFya2VycyBmb3IgZXZlcnkgY2x1c3RlciBjb21wYXJlZCB0byBhbGwgcmVtYWluaW5nIGNlbGxzLCByZXBvcnQgb25seSB0aGUgcG9zaXRpdmUgb25lcyB1c2luZyBvbmx5LnBvcyA9IFRSVUUKa2VyYXRzLm1hcmtlcnMgPC0gRmluZEFsbE1hcmtlcnMoa2VyYXRzLCBvbmx5LnBvcyA9IEZBTFNFLCBtaW4ucGN0ID0gMC4yNSwgbG9nZmMudGhyZXNob2xkID0gMC4yNSkKa2VyYXRzLm1hcmtlcnMgJT4lIGdyb3VwX2J5KGNsdXN0ZXIpICU+JSB0b3BfbihuID0gMiwgd3QgPSBhdmdfbG9nMkZDKQoKY2x1c3Rlcl9tYXJrZXJzIDwtIGtlcmF0cy5tYXJrZXJzICU+JSBncm91cF9ieShjbHVzdGVyKSAlPiUgZmlsdGVyKCFiZXR3ZWVuKGF2Z19sb2cyRkMsIC0wLjYsIDAuNikgJiBwX3ZhbF9hZGogPCAwLjA1KQoKCnRvcF9tYXJrZXJzIDwtIGtlcmF0cy5tYXJrZXJzICU+JSBncm91cF9ieShjbHVzdGVyKSAlPiUgZmlsdGVyKCFiZXR3ZWVuKGF2Z19sb2cyRkMsIC0xLCAxKSAmIHBfdmFsX2FkaiA8IDAuMDUpCgojd3JpdGUgb3V0IG1hcmtlcnMgdG8gZXhjZWwgZmlsZS4gQ29zbmlkZXIgdXNpbmcgQ1NWIGFzIHRoZXNlIGFyZSBlYXNpZXIgdG8gbWFuaXB1bGF0ZS4KCndyaXRlLnhsc3goY2x1c3Rlcl9tYXJrZXJzLCJyZWZpbmVkX2tlcmF0c19hbGxjbHVzdGVyX21hcmtlcnMueGxzeCIpCiMgaHR0cHM6Ly9zY3JuYXNlcS1jb3Vyc2UuY29nLnNhbmdlci5hYy51ay93ZWJzaXRlL3NldXJhdC1jaGFwdGVyLmh0bWwKIyBoYXMgYSB0dXRvcmlhbCBleHBsYWluaW5nIHRoZXNlIHRoaW5ncwpgYGA=